Wildlife Conservation in the North: Historic Approaches and their \nConsequences; Seeking Insights for Contemporary Resource Management
Bibliographic record
Abstract
Recent studies in the field of Canadian environmental history have suggested that early state \nwildlife conservation programs in northern Canadawere closely tied tobroader efforts to \ncolonize the social and economic lives of the region’s Aboriginal people. Although it is tempting \nto draw a sharp distinction between the “bad old days” of autocratic conservation and the more \ninclusive approaches of the enlightened present such as co-management and the incorporation of \ntraditional ecological knowledge (TEK) into wildlife management decision-making, this paper \nwill argue that many conflicts associated with the older colonial conservation regime have \nsurvived to the present day. Recent anthropological literature has suggested that traditional \nenvironmental knowledge is often marginalized in wildlife decision making bodies when \njuxtaposed with scientific expertise or bureaucratic priorities. Aboriginal people may now be \nrecognized as formal participants in the management of wildlife and protected areas, but this \ntentative shift in political power represents an incomplete attempt to decolonize wildlife \nmanagement practices in the North. The paper concludes with policy recommendations that \nmight further apportion power over northern wildlife and protected areas to Aboriginal people.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.012 | 0.027 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".